Freddy Priyatna

Formalisation and experiences of R2RML-based SPARQL to SQL query translation using morph

R2RML is used to specify transformations of data available in relational databases into materialised or virtual RDF datasets. SPARQL queries evaluated against virtual datasets are translated into SQL queries according to the R2RML mappings, so that they can be evaluated over the underlying relational database engines. In this paper we describe an extension of a well-known algorithm for SPARQL to SQL translation, originally formalised for RDBMS-backed triple stores, that takes into account R2RML mappings. We present the result of our implementation using queries from a synthetic benchmark and from three real use cases, and show that SPARQL queries can be in general evaluated as fast as the SQL queries that would have been generated by SQL experts if no R2RML mappings had been used.

Geographical linked data: a Spanish use case

We present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to administrative, hydrographic, and statistical domains. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as unemployment, population, dwelling, industry, and building trade. Moreover, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features.

MIRROR: Automatic R2RML Mapping Generation from Relational Databases

Two W3C recommendations exist for the transformation of RDB content into RDF: Direct Mapping DM and R2RML. The DM recommendation specifies the set of fixed transformation rules, whilst R2RML allows customising them. Here we describe the MIRROR system, which generates two sets of R2RML mappings. First, it creates a set of mappings that allow any R2RML engine to generate a set of RDF triples homomorphic to the ones that a DM engine would generate they only differentiate in the URIs used. This allows R2RML engines to exhibit a similar behaviour to that of DM engines. Second, it produces an additional set of R2RML mappings that allow generating triples resulting from the implicit knowledge encoded in relational database schemas, such as subclass-of and M-N relationships. We demonstrate the behaviour of MIRROR using the W3C DM Test Case together with an extended version of one of its databases.

morph-LDP: An R2RML-Based Linked Data Platform Implementation

The W3C Linked Data Platform (LDP) candidate recommendation defines a standard HTTP-based protocol for read/write Linked Data. The W3C R2RML recommendation defines a language to map relational databases (RDBs) and RDF. This paper presents morph-LDP, a novel system that combines these two W3C standardization initiatives to expose relational data as read/write Linked Data for LDP-aware applications, whilst allowing legacy applications to continue using their relational databases.

MappingPedia: A Collaborative Environment for R2RML Mappings

Most of Semantic Web data is being generated from legacy datasets with the help of mappings, some of which may have been specified declaratively in languages such as R2RML or its extensions: RML and xR2RML. Most of these mappings are kept locally in each organization, and to the best to our knowledge, a shared repository that would facilitate the discovery, registration, execution, request and analysis of mappings doesn’t exist. Additionally, many R2RML users do not have sufficient knowledge of the mapping language, and would probably benefit from collaborating with others. We present a demo of MappingPedia, a collaborative environment for storing and sharing R2RML mappings. It is comprised of five main functionalities: (1) Discover, (2) Share, (3) Execute, (4) Request, and (5) Analyze.

Querying clinical data in HL7 RIM based relational model with morph-RDB

BackgroundSemantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources. Our previous efforts involved materialized RDF datasets, hence losing data freshness.ResultsIn this paper we present a solution that uses an ontology based on the HL7 v3 Reference Information Model and a set of R2RML mappings that relate this ontology to an underlying relational database implementation, and where morph-RDB is used to expose a virtual, non-materialized SPARQL endpoint over the data.ConclusionsBy applying a set of optimization techniques on the SPARQL-to-SQL query translation algorithm, we can now issue SPARQL queries to the underlying relational data with generally acceptable performance.

Applying SPARQL-DQP for Federated SPARQL Querying over Google Fusion Tables

Google Fusion Tables (GFT) is a data management, integration and visualization service provided by Google. Users can upload their structured data, integrate it with other people’s data, and visualize it on various tools provided, such as Google Maps, charts or graphs. Every GFT table constitutes a data silo that is not commonly linked to other data sources. A way to enable data to be linked and reused is by exposing it as (virtual) RDF dataset (for instance, using R2RML) and to query it using SPARQL. In this work, we present a system that exposes GFT tables as SPARQL endpoints, enabling federated SPARQL queries with data from other SPARQL endpoints.

A Linked Data Profiling Service for Quality Assessment

The Linked (Open) Data cloud has been growing at a rapid rate in recent years. However, the large variance of quality in its datasets is a key obstacle that hinders their use, so quality assessment has become an important aspect. Data profiling is one of the widely used techniques for data quality assessment in domains such as relational data; nevertheless, it is not so widely used in Linked Data. We argue that one reason for this is the lack of Linked Data profiling tools that are configurable in a declarative manner, and that produce comprehensive profiling information with the level of detail required by quality assessment techniques. To this end, this demo paper presents the Loupe API, a RESTful web service that profiles Linked Data based on user requirements and produces comprehensive profiling information on explicit RDF general data, class, property and vocabulary usage, and implicit data patterns such as cardinalities, instance ratios, value distributions, and multilingualism. Profiling results can be used to assess quality either by manual inspection, or automatically using data validation languages such as SHACL, ShEX, or SPIN.